Add README.md

README.md

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+---
+license: other
+tags:
+  - heal
+  - horizon
+  - bev
+  - lidar
+---
+
+# SparseBEV + Lidar Fusion + HENet-tiny
+
+SparseBevFusion extends SparseBEV with a lidar branch: HENet-tiny extracts camera features, `CenterPointDetector` (`PillarFeatureNet` + `PointPillarScatter`) processes lidar point clouds, `DeformableFeatureAggregationLiF` (with `InstanceFuseModule`) fuses camera-lidar features in BEV space, and `SparseBEVHead` performs sparse query detection.
+
+---
+
+## Deployment Metrics
+
+### Model Parameters
+
+| Model | Model Input | Backbone | Neck | Model Output |
+|---|---|---|---|---|
+| SparseBevFusion | 6-camera multi-view images `(B,6,3,256,704)` + lidar point cloud `(B,N,5)` | HENet-tiny (camera) + PointPillarScatter (lidar) | MMFPN + DenseDepthNet + DFA-LiF | 3D bounding boxes `(B,N,cls+reg)` |
+
+### Accuracy Metrics
+
+| March | Metric | float | calibration | qat | hbm |
+| --- | --- | --- | --- | --- | --- |
+| J6M | NDS | 0.6704 | 0.651 | 0.6647 | 0.6628 |
+|  | mAP | 0.6086 | 0.5853 | 0.6076 | 0.5961 |
+
+> Results measured with `march = March.NASH_M` (J6M) configuration.
+>
+> HEAL version: heal 0.0.2 / hbdk4-compiler 4.11.11 / horizon_plugin_pytorch 3.3.10.
+
+### Performance Metrics
+
+> **Performance benchmark**: FPS is measured with single-core 8 threads; latency is single-core single-thread; memory is peak DDR usage.
+
+| March | latency (ms) | fps | Memory Usage |
+|---|---|---|---|
+| J6M | 22.01 | 55.91 | 151.80 |
+| J6P | 16.55 | 309.27 | 162.90 |
+| J6B | 85.92 | 19.14 | 84.00 |
+
+---
+
+## Model Overview
+
+### Core Design
+
+SparseBevFusion extends SparseBEV with a lidar branch: HENet-tiny extracts camera features, `CenterPointDetector` (`PillarFeatureNet` + `PointPillarScatter`) processes lidar point clouds, `DeformableFeatureAggregationLiF` (with `InstanceFuseModule`) fuses camera-lidar features in BEV space, and `SparseBEVHead` performs sparse query detection.
+
+- **Task type**: BEV 3D object detection (BEV 3D Object Detection, camera + lidar fusion).
+- **backbone**: HENet-tiny (`type=HENet`, `in_channels=3`, `embed_dims=[64,128,192,384]`, multi-view camera feature extraction); lidar branch `CenterPointDetector` with `PillarFeatureNet` (`num_input_features=5`) + `PointPillarScatter` + HENet (`in_channels=64`) for pillar features (`voxel_size=[0.2,0.2,8]`).
+- **neck**: `MMFPN` (camera branch, `in_strides=[2,4,8,16,32]`→`out_strides=[4,8,16,32]`) + `DenseDepthNet` (dense depth auxiliary) + `DeformableFeatureAggregationLiF` (with `InstanceFuseModule`, BEV camera-lidar feature fusion).
+- **Detection head**: `SparseBEVHead` (`MemoryBank` + `SparseBEVEncoder`, sparse query + `DeformableFeatureAggregationLiF` fusion, `num_classes=10`, `num_decoder=6`, `num_anchors=384`).
+- **Loss function**: `FocalLoss` (cls) + `L1Loss` (reg) + `CrossEntropyLoss` (cns) + `GaussianFocalLoss` (yns).
+- **Model input**: 6-camera multi-view images `(B,6,3,256,704)` + lidar point cloud `(B,N,5)` (`load_dim=5`, `use_dim=[0,1,2,3,4]`, `num_lidar_sweeps=9`, `voxel_size=[0.2,0.2,8]`, `max_voxels=(30000,40000)`).
+- **Model output**: 10-class 3D bounding boxes (`num_classes=10`), `(B,N,cls+reg)`.
+
+### Official Repo and Paper
+
+Official repo: https://github.com/yichen928/SparseFusion
+Paper: https://arxiv.org/abs/2304.14340
+
+Note: The camera backbone HENet is a HEAL in-house implementation; the official repo uses a different backbone.
+
+### Reference
+
+For more J6 chip deployment details, see https://developer.horizon.auto/blog/10003